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The sampling theorem states that a continuous signal x(t) bandlimited to B Hz can be recovered from its samples x[n] = x(n*T), where n is an integer, if T is greater than or equal to 1/(2B) without loss of any information. And we call 2B the Nyquist rate.
Sampling at a rate below the Nyquist rate is called undersampling, it leads to the aliasing effect. Let's observe the aliasing effect with the following Python script:

The blue curve is the original signal, the blue dots are the samples obtained with the Nyquist rate and the red dots are the samples obtainde with 35 Hz. It's easy to see that the blue samples are enough to recover the blue curve, while the red ones are not enough to capture the oscillations of the signal.

Deploying code to production can be filled with uncertainty. Reduce the risks, and deploy earlier and more often. Download this free guide to learn more. Brought to you in partnership with Rollbar.